AKSDeploymentTutorial/Tensorflow/03_TestLocally.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test web application locally\n",
    "This notebook pulls some images and tests them against the local web app running inside the Docker container we made previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from testing_utilities import to_img, img_url_to_json, plot_predictions\n",
    "import requests\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%load_ext dotenv\n",
    "%dotenv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'caia/tfresnet-gpu'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_name = os.getenv('docker_login') + os.getenv('image_repo')\n",
    "image_name "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the Docker conatainer in the background and open port 80. Notice we are using nvidia-docker and not docker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash --bg -s \"$image_name\"\n",
    "nvidia-docker run -p 80:80 $1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wait a few seconds for the application to spin up and then check that everything works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9.0"
     ]
    }
   ],
   "source": [
    "!curl 'http://0.0.0.0:80/version'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pull an image of a Lynx to test our local web app with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGEURL = \"https://upload.wikimedia.org/wikipedia/commons/thumb/6/68/Lynx_lynx_poing.jpg/220px-Lynx_lynx_poing.jpg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "headers = {'content-type': 'application/json'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"input\": {\"image\": \"\\\\\"iVBORw0KGgoAAAANSUhEUgAAAOAAAADgCAIAAACVT/22AAABJGlDQ1BJQ0MgUHJvZmlsZQAAeJxjY'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jsonimg = img_url_to_json(IMAGEURL)\n",
    "jsonimg[:100]  # Example of json string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2f41039e48>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(to_img(IMAGEURL))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.56 ms, sys: 420 µs, total: 4.98 ms\n",
      "Wall time: 2.01 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'result': [{'image': [['n02127052 lynx, catamount', 0.9974517226219177],\n",
       "    ['n02128385 leopard, Panthera pardus', 0.0015077460557222366],\n",
       "    ['n02128757 snow leopard, ounce, Panthera uncia', 0.0005164783797226846]]},\n",
       "  'Computed in 2004.67 ms']}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time r = requests.post('http://0.0.0.0:80/score', data=jsonimg, headers=headers)\n",
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try a few more images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = ('https://upload.wikimedia.org/wikipedia/commons/thumb/6/68/Lynx_lynx_poing.jpg/220px-Lynx_lynx_poing.jpg',\n",
    "          'https://upload.wikimedia.org/wikipedia/commons/3/3a/Roadster_2.5_windmills_trimmed.jpg',\n",
    "          'http://www.worldshipsociety.org/wp-content/themes/construct/lib/scripts/timthumb/thumb.php?src=http://www.worldshipsociety.org/wp-content/uploads/2013/04/stock-photo-5495905-cruise-ship.jpg&w=570&h=370&zc=1&q=100',\n",
    "          'http://yourshot.nationalgeographic.com/u/ss/fQYSUbVfts-T7pS2VP2wnKyN8wxywmXtY0-FwsgxpiZv_E9ZfPsNV5B0ER8-bOdruvNfMD5EbP4SznWz4PYn/',\n",
    "          'https://cdn.arstechnica.net/wp-content/uploads/2012/04/bohol_tarsier_wiki-4f88309-intro.jpg',\n",
    "          'http://i.telegraph.co.uk/multimedia/archive/03233/BIRDS-ROBIN_3233998b.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "url='http://0.0.0.0:80/score'\n",
    "results = [requests.post(url, data=img_url_to_json(img), headers=headers) for img in images]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/AKSDeployment/lib/python3.5/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x648 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(images, results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next lets quickly check what the request response performance is for the locally running Docker container."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_data = list(map(img_url_to_json, images)) # Retrieve the images and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "timer_results = list()\n",
    "for img in image_data:\n",
    "    res=%timeit -r 1 -o -q requests.post(url, data=img, headers=headers)\n",
    "    timer_results.append(res.best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.055485543329268694,\n",
       " 0.05390993170440197,\n",
       " 0.054554368183016774,\n",
       " 0.05410102056339383,\n",
       " 0.05501371566206217,\n",
       " 0.05426212977617979]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timer_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average time taken: 54.55 ms\n"
     ]
    }
   ],
   "source": [
    "print('Average time taken: {0:4.2f} ms'.format(10**3 * np.mean(timer_results)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stop our Docker container"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bcd44f05bea5\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "docker stop $(docker ps -q)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can move onto [deploying our web application on AKS](04_DeployOnAKS.ipynb)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:AKSDeployment]",
   "language": "python",
   "name": "conda-env-AKSDeployment-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}